LPC and Sparse LPC Algorithms and RTL Architecture

Student thesis: Master Thesis and HD Thesis

  • Henrik Holbæk Pedersen
4. term, Signal Processing and Computing, Master (Master Programme)
This project covers the analysis of linear prediction coding, and sparse linear prediction algorithms, with the goal of creating a Register Transfer Level architecture. Two algorithm is then suggested to find a set of, linear prediction coefficients, and sparse linear prediction coefficients, these algorithms are analysed finding the flow, and inherent parallelism. With the analysis of these algorithms a finite state machine with data path, is build consisting of a set of the control path, and data path. The control path is build using algorithmic state machine charts, and data path is build consisting a set of hardware blocks.
The sparse linear prediction algorithm analysed in this report is a novel idea recently presented by the supervisor Tobias Lindstrøm Jensen, therefore a larger part of the analysis is used to investigate it. The given algorithm consists of four iterations where a right hand side least squares problem is solved each iteration. Given this it is found that the Levinson algorithm is the most sufficient way of finding a solution, where other methods are also investigated.
The report ends up with two architectures, which can be either implemented in VHDL or further improved employing different optimizations methods.
Publication date2015
ID: 218017675